Run 10445729

Task 29 (Supervised Classification) credit-approval Uploaded 06-04-2020 by Heinrich Peters
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Flow

sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer, columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num =sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.Standa rdScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing ._encoders.OneHotEncoder)),randomforestclassifier=sklearn.ensemble.forest.R andomForestClassifier)(1)	Automatically created scikit-learn flow.
sklearn.impute._base.SimpleImputer(1)_add_indicator	false
sklearn.impute._base.SimpleImputer(1)_copy	true
sklearn.impute._base.SimpleImputer(1)_fill_value	null
sklearn.impute._base.SimpleImputer(1)_missing_values	NaN
sklearn.impute._base.SimpleImputer(1)_strategy	"most_frequent"
sklearn.impute._base.SimpleImputer(1)_verbose	0
sklearn.preprocessing.data.StandardScaler(29)_copy	true
sklearn.preprocessing.data.StandardScaler(29)_with_mean	true
sklearn.preprocessing.data.StandardScaler(29)_with_std	true
sklearn.preprocessing._encoders.OneHotEncoder(11)_categorical_features	null
sklearn.preprocessing._encoders.OneHotEncoder(11)_categories	null
sklearn.preprocessing._encoders.OneHotEncoder(11)_drop	null
sklearn.preprocessing._encoders.OneHotEncoder(11)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(11)_handle_unknown	"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(11)_n_values	null
sklearn.preprocessing._encoders.OneHotEncoder(11)_sparse	true
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(2)_n_jobs	null
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(2)_remainder	"drop"
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(2)_sparse_threshold	0.3
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(2)_transformer_weights	null
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(2)_transformers	[{"oml-python:serialized_object": "component_reference", "value": {"key": "num", "step_name": "num", "argument_1": [false, true, true, false, false, false, false, true, false, false, true, false, false, true, true]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "cat", "step_name": "cat", "argument_1": [true, false, false, true, true, true, true, false, true, true, false, true, true, false, false]}}]
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(2)_verbose	false
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler)(2)_memory	null
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler)(2)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}]
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler)(2)_verbose	false
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_memory	null
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}]
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(2)_verbose	false
sklearn.ensemble.forest.RandomForestClassifier(63)_bootstrap	true
sklearn.ensemble.forest.RandomForestClassifier(63)_class_weight	null
sklearn.ensemble.forest.RandomForestClassifier(63)_criterion	"gini"
sklearn.ensemble.forest.RandomForestClassifier(63)_max_depth	null
sklearn.ensemble.forest.RandomForestClassifier(63)_max_features	0.41377734573103764
sklearn.ensemble.forest.RandomForestClassifier(63)_max_leaf_nodes	null
sklearn.ensemble.forest.RandomForestClassifier(63)_min_impurity_decrease	1e-07
sklearn.ensemble.forest.RandomForestClassifier(63)_min_impurity_split	null
sklearn.ensemble.forest.RandomForestClassifier(63)_min_samples_leaf	7
sklearn.ensemble.forest.RandomForestClassifier(63)_min_samples_split	5
sklearn.ensemble.forest.RandomForestClassifier(63)_min_weight_fraction_leaf	0.0
sklearn.ensemble.forest.RandomForestClassifier(63)_n_estimators	100
sklearn.ensemble.forest.RandomForestClassifier(63)_n_jobs	1
sklearn.ensemble.forest.RandomForestClassifier(63)_oob_score	false
sklearn.ensemble.forest.RandomForestClassifier(63)_random_state	1
sklearn.ensemble.forest.RandomForestClassifier(63)_verbose	0
sklearn.ensemble.forest.RandomForestClassifier(63)_warm_start	false
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "randomforestclassifier", "step_name": "randomforestclassifier"}}]
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)_verbose	false